SingleCellDesk Example Analysis

📘 Experimental Design Summary

This analysis is based on the single-cell RNA-seq dataset published in:

Tu, J. et al. (2021). Single-Cell Transcriptomics of Human Nucleus Pulposus Cells: Understanding Cell Heterogeneity and Degeneration. Advanced Science, 8(23), 2103631. https://doi.org/10.1002/advs.202103631

Study Objectives:

• Profile transcriptional heterogeneity in human nucleus pulposus cells (NPCs).
• Compare non-degenerated (Grade II) and degenerated (Grade III–IV) intervertebral discs.
• Identify distinct NPC subtypes and reconstruct cellular trajectories.

Data Source:

• GEO accession: GSE165722
• Technology: BD Rhapsody platform
• Samples: 8 NPC tissue samples with varying degeneration states.

Summary of Analysis Flow:

1. QC and Filtering: Remove low-quality cells with high mitochondrial content.
2. Normalization and Clustering: Use Seurat to identify transcriptionally distinct clusters.
3. Marker Gene Detection: Find genes distinguishing each cluster.
4. Cluster Annotation: Label clusters into known biological subtypes.
5. Pseudotime Analysis: Use Monocle3 to infer differentiation trajectories.

Interpretation:

From the plots generated: - UMAP/tSNE: Reveal 6 biologically interpretable NPC subtypes. - DotPlot: Confirms canonical marker expression in annotated subtypes. - Pseudotime: Suggests HT-CLNPs are early progenitors transitioning into mature states such as effector or fibroNPCs.

📥 Choose Dataset Format

This analysis use dataset “GSE165722”.

🔬 Step 1: Quality Control

QC Violin Plot

QC Violin Plot

🔬 Step 2: Normalization and Clustering

🌐 Global Reference Annotation (SingleR)

Global cell type identities are assigned using SingleR, referencing transcriptional profiles from datasets such as the Human Primary Cell Atlas. This method enables the identification of broad cell categories (e.g., MSCs, T cells) based on cross-tissue gene expression similarity.

UMAP Clustering (Default)

UMAP Clusters

📌 NPC Subtype Annotation via Signature Score Enrichment (AUCell)

This approach evaluates the activation level of each NPC subtype signature at the single-cell level using AUCell. While umap_clusters_celltype.png displays general cell type classifications inferred from global references, umap_npc_subtypes_auc.png reveals functionally distinct NPC subtypes based on enrichment of tissue-specific marker genes.

UMAP Clustering (Default)

UMAP Clusters

🔬 Step 3: Marker Identification and Annotation

Marker Genes (Preview)

##   p_val avg_log2FC pct.1 pct.2 p_val_adj cluster  gene
## 1     0  1.8229819 0.868 0.317         0       0 CYR61
## 2     0  1.2440728 0.977 0.431         0       0   DCN
## 3     0  1.8586392 0.877 0.345         0       0  CTGF
## 4     0  1.3793767 0.947 0.417         0       0   LUM
## 5     0  1.7458917 0.940 0.456         0       0   FN1
## 6     0  0.6984552 0.915 0.438         0       0   CLU

DotPlot of Top Markers

Top Marker DotPlot

Barplot: Cell Type Distribution

Cell Type Distribution Barplot

🔬 Step 4: Pseudotime Inference with Monocle3

Pseudottime Trajectory

Pseudotime trajectory of NPC populations

🔬 Step 5: Cell-Cell Communication with CellChat

Cell-cell communication of NPC populations

📌 Notes

• For GSE165722, the .counts.tsv.gz and .cellname.txt.gz pairs are expected in data/GSE165722/extracted/
• For 10x, use individual filtered_feature_bc_matrix/ directories for each sample. ## 📊 Additional Static Results